Content
75%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A strong, highly actionable body: four executable implementations with edge-case guards, concrete threshold calibration guidance, explicit causality and calibration discipline, and a clear output template. Its weaknesses are repetition — the not-a-trade-signal and unpublished-provenance caveats are each restated three to four times — and modest missing validation checkpoints in the mode workflows despite otherwise excellent structure and one-level-deep external references.
Suggestions
State the 'not a trade signal' disclaimer once (Overview or Notes) and reference it elsewhere with a single clause instead of restating it in Mode 2, the output template, and Notes.
Consolidate the unpublished-internal-replays provenance caveat into the Overview and cut its restatements in Mode 3 and References to a one-line pointer.
Add an explicit verification step to each mode workflow (e.g. 'confirm the calm-period false-alarm rate before quoting regime counts', 're-check onset lead times after replacing filters with causal versions' as a numbered step) to close the validation gaps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with executable code, calibration tables, and non-obvious domain warnings, but it repeats the same caveats multiple times — the 'not a trade signal' disclaimer appears in the Overview, Mode 2, the output template, and Notes; the unpublished-replay provenance caveat is restated in the Overview, Mode 3, and References — fitting the level-3 anchor 'mostly efficient but includes some unnecessary explanation or could be tightened'. It is not a 2 because there is no padding explaining concepts Claude already knows, and not a 4 because the redundant disclaimer repetition is more than a minor trim. | 3 / 5 |
Actionability | Four complete, copy-paste-ready Python functions with docstrings, argument definitions, edge-case guards (exit_threshold validation, MAD-zero handling, min_bars checks), a concrete threshold-selection table, a pip install command, and a full output report template — matching the level-5 anchor of fully executable guidance with specific examples covering the common cases. Not a 4 because no key execution detail is missing. | 5 / 5 |
Workflow Clarity | Each mode opens with a numbered workflow (e.g. Mode 1's five steps from rolling correlations through hysteresis to emitting regime state) followed by implementing code, and the Notes plus 'Calibration Discipline' sections supply checkpoints like walk-forward label selection and 'quote the calm-period false-alarm rate as the honesty metric'. This fits the level-4 anchor 'clear sequence with most checkpoints present; minor validation gaps' — it is not a 5 because the mode workflows lack explicit validate-and-verify steps for outputs (e.g. no stated step for checking regime output or false-alarm rate before reporting), though nothing here is destructive or batch, so no cap applies. | 4 / 5 |
Progressive Disclosure | The body is well organized into a clear Overview, four mode sections each with use case, workflow, code, and calibration notes, plus Dependencies, Output Format, Notes, and References — with the deep material (pipeline math, pinned regression) correctly deferred one level deep to an external repo and sibling skills. This fits the level-4 'good structure; most content appropriately placed; references mostly clear; minor organization gaps' anchor. It is not a 5 because ~250 lines of inline function code and the long provenance narrative could live in reference files, and not a 3 because navigation is easy and nothing that belongs in a nested reference is buried. | 4 / 5 |
Total | 16 / 20 Passed |